Model Predictive Control (MPC) refers to a class of algorithms that compute a sequence of manipulated variable adjustments in order to optimize the future behavior of complex multivariable processes. Originally developed to meet the needs of petroleum refineries and chemical processes, MPC can now be found in a wide variety of application areas including chemicals, food processing, automotive, aerospace, metallurgy, and pulp and paper. A well-known implementation of MPC in chemical and refinery applications is Dynamic Matrix Control or DMC.
The MPC Controller employs a software model of the process to predict the effect of past changes of manipulated variable and measurable disturbances on the output variables of interest. The independent variables are computed so as to optimize future system behavior over a time interval known as the prediction horizon. In the general case any desired objective function can be used for the optimization. The system dynamics are described by an explicit process model, which can take, in principle, a number of different mathematical forms. Process input and output constraints are included directly in the problem formulation so that future constraint violations are anticipated and prevented.
In practice a number of different approaches have been developed and commercialized in implementing MPC Controllers. The most successful implementations have made use of a linear model for the plant dynamics. The linear model is developed in a first step by gathering data on the process by introducing test disturbances on the independent (manipulated) variables and measuring the effects of the disturbances on the dependent (controlled) variables. This initial step is referred to as identification.
U.S. Pat. Nos. 4,349,869 and 4,616,308 describe an implementation of MPC control called Dynamic Matrix Control (DMC). These patents describe the MPC algorithms based on linear models of a plant and describe how process constraints are included in the problem formulation. Initial identification of the MPC controller using process data is also described.
By way of further background this identification of process dynamics requires a pre-test in which the manipulated variables of the process are moved in some pattern to determine the effect on the dependent (controlled) variables. In a chemical or refinery process the independent variables include the PID (proportional-integral-derivative) controller set points for selected dependent variables, the final control element positions of PID controllers in manual, and temperatures, material flows, pressures and compositions that are determined outside the scope of the controller's domain. For any process Identification test, the independent variables are fixed for the analysis of the data. Further the tuning of any of the PID controllers in the domain of the MPC controller is fixed. The MPC controller that is built to use the dynamic process models from the identification must have exactly the same configuration of independent variables that existed when the identification was performed. Thus the PID controller configuration that is present during identification imbeds the PID controller dynamics in the dynamic model. Because the PID dynamics are a part of the plant behavior there is an inherent correlation of variables that happens as unmeasured disturbances occur in the process. The various PID control loops respond to those unmeasured disturbances and move many of the controlled variables in response. This has historically always prevented practitioners from creating MPC controllers free of the PID dynamics using standard identification tests.
U.S. Pat. No. 6,980,938 by the inventor is incorporated by reference into this application in its entirety. This application addresses the aforementioned issue and describes a methodology for removing the PID dynamics from the dynamic model by use of a novel mathematical matrix algorithm that interchanges selected final control element position (usually valve positions) controlled variables with their corresponding selected independently controllable, manipulated PID controller set point variables in the linearized model using matrix row elimination mathematics to generate a second linearized model that has a new set of independently controllable, manipulated variables, the second model having the dynamics of the selected independently controllable, manipulated PID controller set point variables removed from the model. This second linearized model is an open loop model based on final control element positions only. Because it is an open loop finite impulse response model it has been shown that it can run 50 to 100 times faster than real time. U.S. Pat. No. 6,980,938 describes and claims the use of this type of model in both control and in the development of off line training simulators.
A greatly desired but unmet need in the control of complex multivariable processes such as chemical manufacturing and oil refining is the use of an adaptive controller. In the past, adding an adaptive mechanism to MPC has been approached in a number of ways. Researchers have primarily focused on updating the internal process model used in the control algorithm. Several articles review various adaptive control mechanisms for controlling nonlinear processes (Seborg, Edgar, & Shah, 1986; Bequette, 1991; Di Marco, Semino, & Brambilla, 1997). In addition, Qin and Badgwell (2000) provide a good overview of nonlinear MPC applications that are currently available in industry. As illustrated by these works, the adaptive control mechanisms consider the use of a nonlinear analytical model, combinations of linear empirical models or some mixture of both.
MPC using nonlinear models is likely to become more common as users demand higher performance and new software tools make nonlinear models more readily available. Developing adequate nonlinear empirical models is very challenging, however. There is no model form that is clearly suitable to represent general nonlinear processes. From a theoretical point of view using a nonlinear model changes the control problem from a convex QP to a non-convex Non-Linear Program (NLP), the solution of which is much more difficult. There is no guarantee, for example, that the global optimum can be found. It is important to note that because of these factors none of these non-linear approaches have been successfully implemented commercially on large-scale controllers.
An alternative approach would be to use first-principles models developed from well-known mass, momentum, and energy conservation laws. However, the cost of developing a reasonably accurate first-principles model is likely to be prohibitive until new software tools and validation procedures become available.
A desirable solution though would be an adaptive controller based on linear MPC type models such as dynamic matrix control (DMC) models. This approach would be highly desirable to a control practitioner who is already conversant with the use of DMC type control.
The recognition of this unmet need and a method of addressing the need by use of an open loop finite impulse response model with the PID set points and replaced with final control element positions coupled with an adaptive control methodology is an aspect of this invention.